Abstract
In the modern world, social media private data evolves as a great asset to business and governments. While social media private data is a boon to the business, it is also causing concern to privacy regulators. We classify the social media data as the business and governments require. The social media private data is classified into two layers: macro level and micro level. The macro level classification is Static Private Data and Dynamic Private Data. The micro level classification includes four types: Personal Identity Data (Static), Relational Identity Data (Static), Personal Identity Data (Dynamic), and Relational Identity Data (Dynamic). Two software metrics “complexity” and “relevancy” are considered. Based on the macro and micro level classification, we measure the complexity and relevancy of social media private data from the perspectives of business and police communities. By conducting extensive experimental research, we study the relationship between different types of social media private data and different communities by the means of the two-metrics relevancy and complexity and justify the necessity of macro and micro level classification. The outcome of the experimental survey is interesting. Police officers are more interested in static private data than dynamic private data. Business managers are more interested in dynamic private data than static private data. While the police are interested in static private data, the business communities are interested in dynamic private data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aggarwal, C.: An introduction to social network data analytics. In: Social Network Data Analytics, pp. 1–15 (2011)
Barnes, S.: A privacy paradox: social networking in the United States. First Monday 11(9), 25–30 (2006)
Batrinca, B., Treleaven, P.: Social media analytics: a survey of techniques, tools and platforms. AI Soc.: Knowl. Cult. Commun. 30(1), 89–116 (2015)
Betancourt, L.: How Companies Are Using Your Social Media Data. Mashable, Australia (2010)
Flitter, J.: Manuscript, 5 types of social data (2012)
Gallagher, R.: Software that tracks people on social media created by defense firm. World news, The Guardian (2013)
Ghonim, W.: Revolution 2.0: The Power of People is Stronger than the People in Power. Mariner Books, Wilmington (2013)
Huang, Q., Yang, Y., Fu, J.: PRECISE: identity-based private data sharing with conditional proxy re-encryption. Future Gener. Comput. Syst. 27 (2017)
Kavanaugh, A., Fox, E., Sheetz, S., Yang, S., Li, L., Shoemaker, D., Natsev, A., Xie, L.: Social media use by government: from the routine to the critical. Gov. Inf. Q. 29(4), 480–491 (2012)
Korolova, A.: Protecting privacy when mining and sharing user data. Ph.D. thesis, Stanford University, USA (2012)
Lange, P.: Publicly private and privately public: social networking on YouTube. J. Comput.-Med. Commun. 13(1), 361–380 (2007)
Leskovec, J.: Analytics and predictive models for social media. In: International World Wide Web Conference in Hyderabad, India (2011)
Lindamood, J., Heatherly, R., Kantarcioglu, M.: Inferring private information using social network data. In: Proceedings of the 18th International Conference on World Wide Web, Geneva (2009)
Manovich, L.: Trending: the promises and the challenges of big social data. Debates in the Digital Humanities, University of Minnesota Press (2012)
Markiewicz, D.: Guidelines for Data Classification. Information Security Office, Computing Services, Carnegie Mellon University (2017)
Moe, W., Schweidel, D.: Social Media Intelligence, 1st edn. Cambridge University Press, Cambridge (2014)
Molla, R.: Facebook has made more than $20 billion in profit since going public—Twitter has lost $2 billion. Recode (2017)
Omand, D., Bartlett, B., Miller, C.: Introducing social media intelligence (SOCMINT). J. Intell. Natl. Secur. 27(6), 801–823 (2012)
Bello-Orgaz, G., Jung, J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)
Patel, N.: The 6 Types of Social Media Content That Will Give You the Greatest Value. CoSchedule, 74 (2015)
Richthammer, C., Netter, M., Riesner, M., Sänger, J., Pernul, G.: Taxonomy of social network data types. EURASIP J. Inf. Secur. 11 (2014)
Roberts, J.: How Companies Use Your Social Media Data When Taking Your Call. Fortune Tech, 1 (2017)
Schneier, B.: A taxonomy of social networking data. IEEE Secur. Priv. 8, 80–88 (2010)
Smith, C.: Social big data: the different types of user data collected by each major social network. Tech Insider, Business Insider, Australia (2014)
Thompson, C.: What companies are doing with your intimate social data. CNBC, USA (2013)
Vallor, S.: Social Networking and Ethics. The Stanford Encyclopedia of Philosophy (Winter 2016 Edition), Stanford University, USA (2016)
Acknowledgement
This work was supported and funded by Kuwait University, Research Project No. (QI 02/17).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Manuel, P. (2019). Macro and Micro Level Classification of Social Media Private Data. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-030-16184-2_81
Download citation
DOI: https://doi.org/10.1007/978-3-030-16184-2_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16183-5
Online ISBN: 978-3-030-16184-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)